Josh Gray: We deal kind of underneath the actual video delivery itself, and deal with more the experience metrics of how the video's getting there, how the playback is occurring. It's a similar kind of pattern, and I wanted to underscore that a lot of the good use cases for AI that we've seen are where you have to meaningfully interpret metrics that are fuzzy and noisy in nature.

The classic examples are things like OCR handwriting--if you think about that, everybody's handwriting is a little bit different, but virtually any place where you're taking large volumes of metrics out of the real world, and doing something meaningful with them, there is incredible noise and variation. And that's a great application of where you can train systems to extract the meaningful patterns that underlie the noise and variation.

In our case we do a lot with playback metrics, delivery metrics, and performance metrics all around the world, to be able to get the clean signals that tell us how we should change the systems to meaningfully improve video content delivery.

RealEyes Director of Technology Jun Heider discusses the importance of internal self-assessment and which use-case elements to consider when choosing a platform for video AI in this clip from Streaming Media East 2018.

Microsoft Principal Product Manager Rafah Hosn discusses the benefits and limitations of a content personalization strategy based on supervised machine learning in this clip from Streaming Media East 2018.

Comcast Technical Solutions Architect Ribal Najjar discusses how operationalizing commonalities between QoE and QoS metrics to deliver a "super-powerful" dataset in this clip from Streaming Media East 2018.

Citrix' Josh Gray provides tips on AI model development and Reality Software's Nadine Krefetz and IBM's David Clevinger speculate on the possibilities of metadata-as-a-service in this clip from Streaming Media East 2018.